Non-negative Tensor Factorization for Single-Channel EEG Artifact Rejection

International audienceNew applications of Electroencephalographic recording (EEG) pose new challenges in terms of artifact removal. In our work, we target informed source separation methods for artifact removal in single-channel EEG recordings by exploiting prior knowledge from auxiliary lightweight sensors capturing artifactual signals. To achieve this, we first propose a method using Non-negative Matrix Factorization (NMF) in a Gaussian source separation that proves competitive against the classic multi-channel Independent Component Analysis (ICA) technique. Additionally, we confront a probabilistic Non-negative Tensor Factorization (NTF) with ICA, both used in an original scheme that jointly processes the EEG and auxiliary signals. The adopted NTF strategy is shown to improve separation accuracy in comparison with the usual multi-channel ICA approach and the single EEG channel NMF method

Review of Artifact Rejection Methods for Electroencephalographic Systems

Technologies using electroencephalographic (EEG) signals have been penetrated into public by the development of EEG systems. During EEG system operation, recordings ought to be obtained under no restriction of movement for routine use in the real world. However, the lack of consideration of situational behavior constraints will cause technical/biological artifacts that often mixed with EEG signals and make the signal processing difficult in all respects by ingeniously disguising themselves as EEG components. EEG systems integrating gold standard or specialized device in their processing strategies would appear as daily tools in the future if they are unperturbed to such obstructions. In this chapter, we describe algorithms for artifact rejection in multi-/single-channel. In particular, some existing single-channel artifact rejection methods that will exhibit beneficial information to improve their performance in online EEG systems were summarized by focusing on the advantages and disadvantages of algorithms

Non-negative matrix factorization for single-channel EEG artifact rejection

International audienceNew applications of Electroencephalographic recording (EEG) pose new challenges in terms of artifact removal. In our work we target applications where the EEG is to be captured by a single electrode and a number of additional lightweight sensors are allowed. Thus, this paper introduces a new method for artifact removal for single-channel EEG recordings using nonnegative matrix factorisation (NMF) in a Gaussian source separation framework. We focus the study on ocular artifacts and show that by properly exploiting prior information on the latter, through the analysis of electrooculographic recordings, our artifact removal results on single-channel EEG are comparable to the results obtained with the classic multi-channel Independent Component Analysis technique

Sparse common feature analysis for detection of interictal epileptiform discharges from concurrent scalp EEG

Temporal interictal epileptiform discharges (IEDs) are often invisible in the scalp EEG (sEEG). However, due to within-electrode temporal correlation and between-electrode spatial correlation, they still have their signatures in the sEEG. Therefore, it is expected to have some common spatial and temporal features among the IEDs. In this paper, we first present a novel method, called common feature analysis (CFA)-based method, for IED detection via an existing common orthogonal basis extraction (COBE) algorithm. In the second approach, we benefit from the sparsity of IED waveforms in developing a new algorithm, namely sparse COBE, and based on that, a sparse CFA (SCFA)-based method for IED detection. The proposed CFA and SCFA models are compared with two state-of-the-art IED detection methods. Two types of approaches, namely within- and between-subject classification approaches, are employed for evaluating the methods. SCFA outperforms the others and achieves the accuracy values of 75.1% and 67.8% using within- and between-subject classification approaches, respectively. This enables the proposed techniques to capture the intracranial biomarkers of epilepsy and ameliorate the performance of a classifier in automatically detecting the scalp-invisible IEDs from sEEG